{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "human-coral",
   "metadata": {},
   "source": [
    "# Generating Mannings N coefficient fields."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "american-optimum",
   "metadata": {},
   "source": [
    "Load an hgrid:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "interpreted-outline",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyschism.mesh import Hgrid\n",
    "hgrid = Hgrid.open('hgrid.ll')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "satellite-aviation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ztZRETzb4LDB33ZvuNdm92z1126ThNm7BJbqQKUsuBLwYMLVKurXaHXsNe6rcmNqCcoqAc1WBVxWUXUe3tNQsD4FmLE5bdnqQ3/3eqg3Xi4EtePQXu1zrzV3463tppq9r6oOTa6W58sXrscGJ9XKrgzBgcsGXuwzFjdP1hdwx4MWsubWCXW75PakCr8qrQw4Z7/LZSQcY73ra9W9ul1R3Te1r0fVCYPddci48fYt73ZlVF1Au8Gwa31pBGxcbm1tezngNk7vPgV6syxqDolaudeez5rrArhR+ufEzgl428ETkIJoPat9pjDlZRI6h+UTjLmAfcLoxZr+T52DgA8BThnX9D2PMBX01vmo6spMMVj6QaWvO/eZqrJtqLTP3bcEujHRX1gc/39ibbzLCF+8LB2fNnO9brJB2h9KHgJdaaByCrHa7+y6QS1mQKfUBxNJ0BSqx8M4BbqP5EDfAhcBFxpirReRS4EzgEifPLwKbjTEnDL9Re6uIXDX8mlnVnMh2UzUk3IkE9yPSelzNjsn5xuDcbiuMz/rari00bbATI/b1UNYy9FlgIXilloy44W78gQXCKYhpdynwfNDz5ffN0Fq/3sfG5WYBu1I4laTvEXxZwBOR3cALgXcCrxcRAZ4LvHKY5ArgbUwCzwDbRGQZeBSwH7ive7OruurRjx6/t+yiX/12Xz1poK04Pcble/NIbPLBhZF+OsK+Fsp3/7njfKElIb5wN4312/3YLOuAsMV1IE0G7EL5Sq073wmxeUJxqb0LMxcoMdj1BZ8pWG85yrXw3gO8GbAjObuAe40xdv7uDuBIT74PAy8CvglsBV5njPkPN5GInAWcBbBnz57ctlcVSL+vzt7s+rOC9o0j1u9OINhJBx/MdDrXCtP5NSxtW/RjXDC6D3SczquPIbTZNO5exwc/eKPdMWstlK4EeL59DK4QX8RsVWLlhcJy/F21BtBLAk9ETgbuNsbsE5FnF5b/dOAR4IeBRwOfFZG/Gn7E+4CMMZcBlwGsrKyYiVKqWsldE2e7h/qtInoBsO8dcXoCwTeZoOHoLtDXa+l843ruRITNA+OWZWjiwe3C+uJ1urHPFpZALTd9TpoY6Nzuq4acDo+1021zbB8K8/lDYV2k/91SaXpSjoV3EnCKiLwA2EIzhncxsFNElodW3m7gTk/eVwJ/YYx5GLhbRP4WWAG+5klb1VHuc68WWL7JBTsu5lpgPiDpyQXf5MHy8qiHZt9abLft25vxON9EhVuO7dpq8Ok0g8Gk5ZYDuwOgC1lbObBz43Oh58aXLEkpaYNOE8oX22vNAna5Zc8aeMaY84DzAIYW3huNMaeJyLXAqTQztWcA13my/xvNWN+HRGQb8JM03eOqHmQX/+o/Sgs0a8W56+Nc2GgAhV6rFArX1pVtgwWebY/thmowuTOwvq6oldsF9nVRlz1X8QToXIspF1L6BIeg4wsLhafeeecDsW6zLtP3QH8J9GLuUJmz0BTr67IO71zgahF5B3AjcDmAiJwCrBhjzgfeC3xQRG4BBPigMeaLHdu80HK/DeFabbbbuXPn5GcFfaCLTSxooPgmHWAceKurcN99I2vNvhwAJh8t83VFfT2bnAkKGTwynmEw4MDkQ86W+445d98VeDEAunXFZnJdlVp9rjtW3jQ1g3rEmPkaMltZWTF79+5d62bMjXQ31Up/iEaPv+kJB3e8LbauzbfA12dB6eUhVjbNQw8136W4557Gytu+vXnLiR2D27Spec2TOykS64q6e92NHXtAHxq3O/ORAlIMJDnWUE75vrjUQmNf3hLghdqbOp6ccqahVPlvfSsiss8Ys9K1qi4WXtUUpB/dghG89GNa9nVK2qLLsd5cS01ba+5sqzWUHnxw9E47/dSEa+VBk1ZPMmzf3kBOjw+mJhliwBubWQ11/bRC1lIMILE3k+hydZou0Eu9WADiY336JPnaGHLnwi4nvotmZT0OVYG3xtKPbFnpGUr3bSJbtzYQceGlx9hS6+Hc9OC36O67b3T/u91bd2zt/vtHabduHX1zwnZrfZD1we4A2MDp0iWApQcSrXz+lFUX6lb63F3Bl/uCzxgcbTp9MnOsuBDMXa21dddz/RV4M5YPcBYisacb3Ee6XNhp0Llv+dBxMGlxwbix4JsJ1TO47nq6wWBycbK2RH0zslrW731u1SoEhhTk3LicR7hKH7KPuWMASwHRl8f3XG3ouEvAl9K0wBcqd0r1VeDNQIceOv776eUi+mkF15LTkHOhoyEC42vd3C9kud1IvQzFSltyVo95TAMuK9/TFvZ+PPzwxpqz8LNdWvfbELa9B6w4SAMoBQStmD+3ntj753JB53PnvAg01s5UF95VrkVXApc+QTRj2EEF3lR07LHNwL3dYPJll75nUn3Wmw9yujs6GEzOpOoJi9AkhH63nIWUlU23Y8fkW40t8HzDRjt2jPJry9PmnVgqoruhKesnBRSrmPWSCzzdFl1OqA2xsNBi4hTkYufEd2yuSi26KUImWH5X+LZQBV4P2rZtvFuqu3dW7tMM7kLf0Hq42GQENNeHBZFe5xZa3KsnJ5aW/Eu5IDyhoEEWmnAA//q54AJg7Y5BJwQVrRgUSoGXC1t3H+oS57pTbfAdm6tS4OWU2UVtgffud/fajAq8FnKXiliAuA/fW7nxsSUirtVn8+ulJjbexrlQ09BzAeVafaF7wTcHEBoT1G30WX5B2Ln7mPWRc+OHrKdQOX1Ar81D/Dkw7AK9FOBKYNmHStowTehSgZctd7mIrxvqPuGgnzLQ43K+5SG+mdTl5dH9pLuT7sSEO9Hgzqa66+w0uKxcPvh44c4A+yY4xsbmtHKsoxRkcqyrEBxK6rCb7+kGX3jOPnXcOefC126b3p190nE+5YClL/jkAm/KsIMKvKjch+/diQS9uVBzn0sNdVPdDcYnBey9pScaSoHnG8dLWXkh4LnW3cQSElelN3zqho2BLwYEX52lW5snMmJxXaGnFRqbiEEkFzBdQdQnhDuqAk9Jf4gGJmdTXZj5Jhn0RINeLhIDnp4wgPGZVvtQvm9m1wWZbbOvm2sB5YOfq9A9ZdMftGTGE/qUAlVqH4NZTpo+oacfEA61xXc+pg291DlPhZemKUkXS98lrKMWHnj661sWBr71bxZ4euLBN6ngWle2XHcxsTuLqeXOwto/bx9gXZi51pvb5dRxY69LggPug5aWOGh5CYMciJr4FKGTJ+lvs8+9sVNQyKlDb6nnW2MA9tXjtrPk3OQcV875yVUfQEvFV+DNVtu2jZ9LPavqwk53TW28z1rzAU53F3V+W4ZNa2XTut1MbTnqbq2vm+paej7gLS2p15jriu3NNEwsPhrr9LHwEksnFJ+ymHK3WBti6VPPt+YCL9WGWBpfXaH0vvNUEleSpm19awg7WCDgaUsOJl+b5MLIt1ZOLzvxAU8DRi8b0SCD5j5yn0m1aWwX1oZZePoArAFr9yHA6bCxbza4jbQ3eqi/qxum/W58TnjJPmXV6HSlMErl80EoVG9Ou0sBGKo/9Gfg8/s0bfh1gdtgAJdfXlZ3hjYs8FzA6TEwF3YaeqFHt0Kbb8xMw84aCfajNA89NLLu9LITex3b8txurAu7kFUX2wffDxe7YUOK3WCpm68t+HT+mPXjS+u6fX4d7k4CxM5ZDmBLjzMXqql8obCS+Nw0vnSl/pK6WmhDAU+/ENOCwNdFdd8fp6260KSCO9uqZ1JD42b2+rT3jn3qwspdy6YBaX9z19r0LSvxTUBMLBPJvenddL4FeSGlbjy3/FCelLVTurnHmSpXtyf1BxE7n13AHmpjKp+bPzd8WvGptlXglcm15PRYlws232yr+4SEz4oKLS/xjZ/ppR52s6Czlp2erHBnaN1rWM/YhpaXaNhNPKOq99YduoliN5VbZkix+By4pdqbqjsEoVi97hZ6QN9Nn6q7b/DFjsN1u20rCU/FleaN+Svw0nIXAVvIaZDFJiCs210y4psB1TOv7qLbGHz0tb9ly7h/06bmuVP75l8t1x+zIg9MQARmWyfc1p8LtdKLMefmC6XLAZ/dh7bc9oTK8uVPHUcKsqE/l1BYrK2pOLdtoTbnpk3F5ZZXgVcu940jPoj5gOdbO+dbo+Y+1+qOzYVAB/4lHzC6nl0Q2hnb0MyqW6aO03UHH9dy3aG4kCUVSuPzx+JSN10u7Nw0KUC55yTl1vlDb0qJ1RtqQ+gYUxD0nYMuv00uVNqCr+TPJjfuqqvC9XXQXAPPfizayjfp4ILPnYRw38emLbfQY1cuXFLr3qx83VobDunuaGwSQpcxMfkA+TdBDCRufA6IYuEl0M29QdzwlHWV6w7BLqfeHLDPAni+tLHwrvlj4W0gl6qnB80d8G66CXbtGh2zO+YWeymmb7Fw6JlS99ErKxdE7vKTUPfVKjaDGgJkDIB6751lzYVSzsWa+y/e5obyWUypcmPH0QUybjtC9aSAFktfAtvQceW4ff5UXBfwtYXpegOeiBwE7AXuNMacLCLH0HyicRewDzjdGLPfyXMa8CYV9OPAU4wxN8XqWloaPdHg66qGlmr4JhZ8s6y+LqqVPte+9XY+MOl8sfE29xhDQJwEnKrA90C7r/E+f0htLK8SgKZg1+YG9FloMXD58miFFhm75ZTejCH45bSp9NzH4kr+THLCS0Facp1ee62/HT2oxMI7B7iN5kPcABcCFxljrhaRS4EzgUt0BmPMlcCVACJyAvCnKdgdfDAccUQYeHoZSegBfA02d9wuNh7nXoMxC8/Guxaer/vqWpE6nS7nwLicbUToRsm9SUqUC72UP5Q3dx9rV6g+nz9lZemwUFc2B36lm68tOee59HduC6lY2SXXQcqfC/CelQU8EdkNvBB4J/B6ERGaD2y/cpjkCuBtOMBz9AoaizCqgw9uXhdun1t1Jx5ikPNBz52IcEHkWzs3doJUObFup5XPYssB3thMa+wmCd24Wj4C58hXXgiuoTpjlkuOVeMrOycs1r4YYNwlKL7zHGurr025wCv5HUv+EGJxXQDnC2sL6XkGHvAe4M2A/QTNLuBeY4xdjn4HcGSijJcBL0pV9IQnNBaeteQs9HIBF7PifFZdrNtpFRu/gzj8QsDzftNBu3PDdBk++cJDB5oCXihNKl0u/HLantO+GFhc2OlyckCXA7RU2tS5iB1nSNMCXyn02kBunoAnIicDdxtj9onIs9tUIiLPAB40xnw5EH8WcBbAnj17eM5zRks37PINmFz0GxuTcy2s1ExsaGwOJqGVsvTcdNF1ctafumFzYZcDuMHAH+bzl0DQDSuFXqj9sfaF2pM6b6FXx+syY/AMtTEGTV+ZofOQ4w+1ITe87Z+ML6yLW/s//nF//T0px8I7CThFRF4AbKEZw7sY2Ckiy0MrbzdwZ6SMlwPBhTXGmMuAywBWVlbMEUc096N+1hQmFwmXWG65C4ZtfSHrLWXhaY54l49AHC6l4POV40qHh/ruvjwpIKVu3BAsSq2bWHgMLKG6QhM/KWCF6sn9rWJtyz0HfUOvpJ6u0Cs91ikoCTxjzHnAeQBDC++NxpjTRORa4FSacbkzgOt8+UVkCfgl4Jm5jXrDG+CDH/QP7PtmZUPdVp0vBsOcbqoPeNoPgXE41239WjmgKIFFTINBfFwvdaOH2hxrv3sssbK73GixusD//YmcNsZgVgK90DnJPc5YmE+zAF9b97wCL6JzgatF5B3AjcDlACJyCrBijDl/mO6ngW8YY75WUriGU+jJB3ciItRtbbPZNqT20YfzQxe9Tu/mDe1LgJe6eGLA87WtLaxDbU/lCR1D6TmMgS6nbTm/ZQ4EU/lKjjk3LpWma3gJ0HLd11/vr7tHFQHPGPMZ4DND99eAp3vSfAz4mJPnJ4sbFuiSxiYmfNad3bcFnm9sLjrhoMNCfp3XdbeFn6/MkErS5Fysue2MQTunzlQ73PwlXxQrhV1ufAkEfceb81ul0qXKyAVtG4i1yTdFdbHwpqrTToNrrhm37nyvbkqNy0E34HnfPhJy59w4sbwlaXzpfP5cxS7w3JsyF86l56SkDS7kUu3T7WkLs1CYLzyUN+fYctQGbrG4WFu6wk2HffKT4Xb1qLkFHvhnY33jd7GxOSiHnbebahW72dvc6L56Sm9WX3tiGgyaAw2lLfkXzoWJzx06jpw4K3dpSaqs3Dbm+HPiUir5HUvAl5M+5/f3hZWe31z3DDTXwHvJS5pZat8iYveV6j7g2fAi0JXckK6/BHSl9fQBvFx4tU2bC73YPhYXs95yjyEUVgq/vrZQ23z+3LjSdDlwbXNeS+JnpLkGHowWHec815oLOzdu4rODvovRdYfiusAvN6wERm3Cc4851ra2ezfMtzg4VHfJsYTydgFeThrfMZScb59KoJFK2wfoSvPccEO8TT1q7oG3fXuz931LAvJBFwrPegNJWwCUAq/rv2VpWKz9JXE5cI4BJlSGu14uJ08f7Y25+9jc8kqPJaY+4Jfzu/cFvxlr7oH3jGfAl7402ZWFNNR8aaKggzzopfwloOt6sbS5aEuOoU26UnC7e/cpCDdNzu/TR9tjwPOFpeCo6wqFh9qdCu87bds/i1BcyP3Zz+a3sQfNPfBg9HiZ++1Xuw8tJWkNutQNaRWDSApufVwssXak0vURl3sjpOJ9oLP7GIhyoZc6plCb2oItBrxY+3PamQqPKZUnpx25v3PJ9TBDrQvgHXss3HXXJMzsPhQWBB3kXZTWrfeuOxSWc9P6yi+FSyysTXl9wS4W5wvL7b7Gfjdf/a5KwJeCVFfgufWE2l96TDnq43qJheXk/fu/L2tzD1oXwIPR1718cLP7kNUXHKdLgS72I1rFwkoA1xV4obiSf9qusGtTRslTENqdAkdIpcBrAzhf+liZOe0KheUcY0na0t885/rNvXZnoHUDvEc/Gh54YOTPAV9297UEeq5by3fxlIKuK/By8rW9qPvwu6Bz25X6k1gPwHOPt+T4Ym1LhYdUcr24/tzrpxSCn/tcvE1T0roBHkyO32n3GOSg7B82lFan0WGuci6Y1AVeAqNYW3LKWEvgxRYK54AhBbsuN7evDW2BFwJfCfBSx1MKvliettdEKejatLlHrWvgjcONsgssdUFDeu9TzgWQc2HE3G3akKovVldO/aWQC9Uf+k1cfyk4QvW7YX0Dr02bS8FXkiaWvm/QVeB100EHAY880sAN8kCSe0PlpLUqAUJfF0ZX4LW5gEvqd/0aciX15wCny3Gk2p4D12nALvf3zQVGG0CWAE+7S6/rG29Mt21KWlfAA9JvwIjFxS68nIvSLT/Hvx6Bl1tfW8il2uADW8hf+lulwqYFvNJ2x9paEp9K1xZy2t3mXlwjrT/gbd48mr3wXTTa3RZ8OWW4dfn8ueBpA9XcsFzgpeoPhXWFnM/tA4wbF8vX5lhiwHP9pTAMHZev3lB7Y+G58aE0fYEuFmf3N9+cbuMUtf6AB/nvOQvFlQAwVJ7rTvlL2phTdm5YW+CFyo8BLqdNJTdRKSBj6VJxblkxoPnyxZR7feVAbxrxXYHnC4sd2xpqfQLv0EPh299u3LmAytmX/GhtYJfbxjZ1dK3bV54Lt1CdXdodal+J2xeXqivkj9Xl8+dsucfqa1MorI/4Pq6Tkvvrllvi7ZyB1ifwoOxttlZdoJfrzk3bB/Da1h2rM2XB5YblQs4XV3ruu0A9VXcIdiXKOaZQ22NllsaV/vHkuEvutznQ+gXeD/0Q3Dn8UFoJqHxpYvlLyvP5+3L7/DlhOe6cZSOl9ab8JTdSyp0qI+ZuW6cGYWhzVXK9hdrqUwn8+r42c0A3GMBtt4XbOEOtX+ABHHkkfOMbI38KfL7w0n3MnfLnXNSzAl4Icrl15KbtCr2S859Knwu9Utj6ytPpSo4zVl6buK6/Rxf4ha73NdT6Bh6Ef5hSYLUBXp83d1t/SZ5cyK019GL7HAD1dbPmukP+2DHHYJD6AwqlDYV1vS5zz21o/4//6G/7GigbeCJyELAXuNMYc7KIHEPzTdpdwD7gdGPMfk++HwfeR/MB7wHwNGPMQ300HoCjjoJ//dfGHbvwU5DqCrzci6z0Am0LIx2W+92HWHml4dOAXhuQ9VFXCnah4/ddj23BHAvzhU8DciF37j0yByqx8M4BbqMBF8CFwEXGmKtF5FLgTOASnUFEloH/SQPDm0VkF/Bw92Y78v24sRsllT62T4WFyg/F9Z3eKseaawO9WFyfN13OPvQ7tPmt2sDOPbYU/Hz+Nr9NKHyWkAu53f0//7O/7WukpXQSEJHdwAuBDwz9AjwX+PAwyRXAiz1Zfw74ojHmZgBjzLeNMY90bPOkjj02/Y8aurBLYOfmmcWWUy80gNNb1/bG8rvnN5Qn5k+52/xmJVDJPfYcd+y8aPnC3HaVtNdXV+lv2/ZYc+qYM9hBvoX3HuDNwCFD/y7gXmOMNSPuAI705HsCYETkeuCxwNXGmN9zE4nIWcBZAHv27Mlu/Jh+9EdHJ9h3AU5jnwqLpXEVyhPyly4fKWlnqm2p+BJ/bhtzgJhbVy5Yc9wp0MXCSs+B7/hyj3ka7pI2zomSwBORk4G7jTH7ROTZLcr/z8DTgAeBT4nIPmPMp3QiY8xlwGUAKysrZqKUXPV1sXfZ57pL2m4VWz6SKiN1wabqjoWn6s5tj69tffwJlZZf4tb+EBBD+XLbGHOn4tcKfnZcfc6UY+GdBJwiIi8AttCM4V0M7BSR5aGVtxu405P3DuBvjDH3AIjIJ4CnAJ/ypO2u446bXO+Te7GvNfBC8W2fVS1pRx+Ayw1rYzWU7NsCseT3DEEtB4qhuNQxu+5UG/t257TJFz9nSo7hGWPOM8bsNsYcDbwc+GtjzGnAp4FTh8nOAK7zZL8eOEFEtg4nMJ4F3NpLy0M6/vjJsLYXe9sbLudGCMX7xuJ87cwtLyfcrSMVHooLlRGDQej8TwN2OecqVmbuec/Zcsso+b37aEsf9d1+O/OqLuvwzgWuFpF3ADcClwOIyCnAijHmfGPMd0TkD4DPAwb4hDHmz7o2Oin3prRhrjsW1uaGSoVZ+Z5RDbU1dQypunLjUuGhtCXpU20u/T2mvW8Lu1B+n7vteenqnka5g8H4gwBzKDGm/ZDZNLSysmL27t3bvSD7Gpq2F2BfNw7kP4TvU+lFmoJaCcy6AnE9Qi8XfDmwK722Ss5JrrvP/DnppwS84dj/Stdy1v+TFiGdeGLzZtXUxdUWdjkXbWqSIRYeS1dyAYbq6Aq+LmnXGnpt0/YFPLfs1LGvpTsnnd3f6RvGny9tXOBBPxdb6Q0CZc+p5sTFysq9aLuGTSPtrADXdl8Ku1LgxfZr6Y75Q3nWAexgowPvqU+Fz38+/iPnXOixeL1PPcLl84eUA5LURVhS/1qBb16gF7su3Hjf8ax34KX8Mfc3v8l60cYGHsDTnjb6wnkO7ELxqQvUfT9frNxSlf7b5kBurcGXA7hYXA68+gJhCGQ+EIbi24BuFmBrE9f1el5DbXzgQRnYfGGxm6kN6NYCeiVhs0rbFWyl+5Lf3xfXZsupI3UuurrbpsvJd9ddrCctBvBOOgluuGHkT91obWHX5uItUZt/3GmAryQ8VVfOOesDbl3L6Aq83PalwmLuPvLkljkYwLe+xXrTYgAP4FnPgk9/unHn3lyhC7PNR4Ri7hK1gV7XsFh4l7Jz4ZObdloAnAbw2h63G57rTsWVlrkOYQeLBDyA5zwHPvnJkT91Q/ncPtjlgq4EMCm1hWofkCsJz7UeprHvK29fsOsCvK7urn7tXqewg0UDHsDzngfXX9+4c/5RXdj1edHGwnLU9kIvbUesfbl5ckEXi2sLua7l9QW80vZ0dffpb3uNzpkWD3gwftFZv967YbEu7LwAL1VPCczaAC6nrFzolf4RTQtyrj8Xcr42l7Q1dQ5cd0lcqd91f+c7rGctJvCe/3z4+MdH/tiN5r5MM5Y+BNKY21Up/Eou/Fj5peGleVLnoo99XwDMAZ4vLhbWph2x81YS19Vv3escdrCowAP4hV+A667z/7Ahi866u16sOVDrAr7UxRwrvy2Mc+rIAV0sbtqQ0+4Y8GLHG8rfto0xd0lcqd91f/e7bAQtLvAAXvQi+MhHGnfsKYm2wMt1p5SbNlV+X+ArzZs6B6XnsxQg0wJeaitpoxsXO36tafqte4PADhYdeAAvfSlcc03ejRiLK4Wfz59SGwh1BV+X+FzQ+cJK9m2hMk3gdW1Pzvmbhb/0Gp1zVeABvOxlcOWVI/80QdfHxdQVPqlyusAv9+bp+qcyLQD2Aby27cg9L667b792f+97bCRV4Fmddhp86EONuw/ghcJCadpo2uDLqSOnzNQ5aQuBPiGn3X0BL6cdqTS57r79gwE88AAbTRV4WqefDldcMR3QlVyoJeoDSLllldY1beh1gZwO6xN4Oe0oSRM7f667q1+7NyDsoAJvUmecAZdfPvL3BbxQmpC/rWLllECuLUjd8GmAzt13gWIJ8HSbfWnbtidnn+vu6t+goLOqwPPpzDPh/e9v3F1u0pg7x99FfYEvFZdT5jSg1wVyvvbmwM4HOl98ThtT7Uudq5i7rf/732ejqwIvpFe9Ct73vnY3aK47FdeXQuWWgq9NfMn5C8W1BWBuPhdcblwobQqObdqcOh8xd0mc618A2EHGZxqtROQgEblRRP7P0H+MiHxORP5FRK4RkU2ePEeLyPdF5KbhdmmfjZ+6Xv3q/Is+518+VY5Wqq6SLVSmLzzVBlelN39uXA5AUmWVlB87F7nnVftz6g6lTZ3XkLskTvsXBHZQADzgHEB/5fpC4CJjzI8A3wHODOT7qjHmJ4bbr7Vs59rpNa/JA0pJmpA7BpZSdQVfDvxCeWP1p6AU2ueWF2uDr2xf23POR+yYc+uOHUPsXPUJvR/8YPK4NrCygCciu4EXAh8Y+gV4LvDhYZIrgBdPoX3zode+Nh90OswXX5qu7aZVCrjS8nx5td93rKmwXGC0gZwPeqlzkhsXO67SY4idq7bXmvYvGOwg38J7D/BmwP7Ku4B7jTH2eaw7gCMDeY8ZdoVvEJFn+hKIyFkisldE9n5rXt+1dc45078oraYFvz7AlwOBGHByAOfGp9rSFjSxY0+dG99x6nJL68w5llh86lpz/Q8/PHmsC6Ak8ETkZOBuY8y+FuV/E9hjjHky8Hrgf4nIDjeRMeYyY8yKMWblsY99bItqZqQ3vCEOLx1W6o7ddDmaJvhidaTOQ1sotQVhSXkpiKXOgRvupmnTzti5KomP/Q4LCjvIm6U9CThFRF4AbAF2ABcDO0VkeWjl7QYmPkxpjPkB8IOhe5+IfBV4ArC3p/bPXm96E1x4YeP2/fNqdyg+x9+HUmXG4tvEhQDhumNhOeBqE5falwLOrTPkL2lb6rppe43p8EcemTyOBVISeMaY84DzAETk2cAbjTGnici1wKnA1cAZwHVuXhF5LPAfxphHROTxwI8CX+ut9Wulc89t9hdcUHYRlkCuTwDOEnyl0GsDgr5h57Y1B34p0PUF6VJ3DHrGTB7TgqlkltbVucDrReRfaMb0LgcQkVNE5L8N0/w08EURuYlmguPXjDH/0aHO+dJ55zV7342Qcvv8sZvNKpQ+tqXyx+Jz4nLCSqHky98mTWl9uefCPaaUvw+Q57hDdVfYASBmzk7EysqK2bt3nfV43/72kbv0n1krBroS5ZaTAmtJXE5YzCLpAw6l+1yIhv4k2vhLjycVluOes3u8jURknzFmpWs5XSy8Kqu3vrXdv3GuBVG65eaPpYu1o22Y7xzEwnLiup4nH0xix+3GlfhzjzX3XFktLU1ug0EDug0Auz5VHy3rS29/O/z2bzfu0M2TistRSdo2+WLpctueCssF2DTS5pTlg4pbVygs5i+FveuGEdCWl5vN18aHHlromdiYKvD61O/8TrN/y1vy0pfCpW1ZbdKG0pQA2wcH151zs88Sdm57cyCXOtbYccXaY+EGk+7lZdi0aRx6q6tw58RiiSqlCrxp6J3vHE1o2O4FdANNbv6SdF3KbGvtpYC2lpaddufAr4vfjdNA88kF3qZNI+AtLcH+/XD77eH8VUAF3vR0wQXN3oKvi3Jgk5u3S9o+IOf6u4CrNE+plZdr4bXxw+S4m5YPfjbMZ9199auT6asmVIE3bV1wQT70fDdFKD4WllN+iWZl3ZXup2EFuvlSYW39GnTWSotZeDYPNLCz7ttuC6evmlAF3ix0wQWjxcpWOTDI9efGdUkbSt8GhvMKuxwLr9SvpS0038xqyNoLlVNhV6wKvFnJPo72pjeNh5d2/2JpXfUBtNx0s7bw+krT1sLLAZ3tcuq4LqCzqqBrrboOb9Z617vKLYecm0uXGbIyfGli6UN524Zpf9f9LC0833lIHZtdNqI3PdHgbhV2M1G18NZC7353s3/d68bDS6w9nz8UFlKpBVha73qx8EJhIRCGjm0w8Ftsdqa+jTVnVUHXiyrw1lIXXdTszzlnPLwP8MXCQ+ojfUlbpw29LrALtTdmzaa6qaWQs6qw600VePOgiy9u9q997Xh4Dixi6Uvjc5QL19y2T7Nb2yZfym3lwstnwbWx5LQq6HpXBd486Q//sNm/5jXj4THQte3WTgt+ueBr2/0syTMN2FmAWcC54X3AroJuaqrAm0e9973N/uyzR2FturVtoNZXnjZd2VhcHxZeG9jB+NMyVnpczvrtvlp1c6s6SzvPuuSS8LhRyu8DTs5Wolg9Pn8JdHL2qTRt63XdPvkWDpfMtrq67bYKuxmoWnjzrve9b+R+1atG7tTNmbph++jS5tYfA1RuWFcLrySv73hij3p1UYXcTFWBt570/vc3+zOHnwCep25tTntKLLVY+q4WXins+laF3JqpAm896vLLm/2v/EqzzwFfLLyNYmXF2tMn9PqCna/eaaiCbs1Vgbee9cEPNvszzmj2ueBLxbVVCehyw0JpSsCnt1T7+laF3FypAm8j6IorRu7TTpuM73Izd+0SW9CsrjZbKJ1vPCwXYKEuqS+fr45pwK6Cbi5VgbfRdOWVzf4Vr/DH931zp6xIC7v9+5u9D0ippxF8oItZfL68qbb2oQq5uVc28ETkIJoPaN9pjDlZRI6h+SbtLmAfcLoxZn8g7x7gVuBtxpjf797sqqSuuqrZv+xl/vhp3vwaMBp2dm/jUi+91H4Ly5gVp/eh+L5VIbeuVGLhnQPcBuwY+i8ELjLGXC0ilwJnApcE8v4B8OetW1nVXtdcM3L/4i+W5bXAcuUuto3l1bCz7tXVuFUXKjfUldXuOstaFVEW8ERkN/BC4J00H98W4LnAK4dJrgDehgd4IvJi4F+BB7o3t6qTrr123P/Sl4bTWjDt3z9pjblvAtF769bjdhp02srz5UtplmCzqoDbMMq18N4DvBk4ZOjfBdxrjLFX7h3AkW4mEdkOnAs8D3hjqHAROQs4C2DPnj2ZTarqrI98ZNz/kpdMWmZ6ssH32iO9d8NsWRp+azFT2kYVchtSSeCJyMnA3caYfSLy7MLy30bT7b2/MQr9MsZcBlwGsLKyUr8cvFb66EfH/c94xmQXNBd6+iH70PjbvKlCbsMrx8I7CThFRF4AbKEZw7sY2Ckiy0Mrbzfg+yDmM4BTReT3gJ3AQEQeMsb8US+tr5quPve5ybAnPrHZx6Dn7jX05kkVcAunJPCMMecB5wEMLbw3GmNOE5FrgVNpZmrPAK7z5H2mdYvI24D7K+zWuW65JRx3/PFx6K0F8CrUqpS6rMM7F7haRN4B3AhcDiAipwArxpjz2xS6b9++e0Rk3r4ofBhwz1o3YgbaeMcZHkrZeMca1kY41qP6KESMqUNmKYnIXmPMylq3Y9palOOEeqyLqvo+vKqqqoVRBV5VVdXCqAIvT5etdQNmpEU5TqjHupCqY3hVVVULo2rhVVVVLYwq8KqqqhZGFXhDicg1InLTcPu6iNw0DH+eiOwTkS8N988N5P8JEfmHYf69IvL0mR5Aproe5zDta0XkKyJyy/ApmrlUH8c6TP8GETEicthMGt5CPVy/7xr+pl8UkY+KyM5Ztn9mMsbUzdmAdwPnD91PBn546H4SzfsAfXn+Enj+0P0C4DNrfRxTOs7nAH8FbB76D1/r45jWsQ7jHwdcD9wOHLbWxzHF3/XngOWh+0LgwrU+jmls9Y3HjoavvvolmtdfYYy5UUXfAjxKRDYbY37gZDWM3hV4KPDv025rF3U4zrOB37Xhxpi7Z9HeLupwrAAX0bwpaOLRyXlU22M1xvyl8v4DzWOjG061SzupZwJ3GWP+2RP3UuALgRvjN4B3icg3gN9n+PzxHKvtcT4BeKaIfE5EbhCRp021lf2o1bGKyItoLKKbp93AHtX2d9X6VTboC3sXysITkb8CjvBEvcUYY//BXwFc5cn7RBpT/+cCxZ8NvM4Y8xER+SWaZ4t/tnuryzXl41wGHgP8JPA04E9E5PFm2BeataZ1rCKyFfgtX9xaacq/q033FmAVuLJba+dUa92nnqeN5ma+C9jthO8G/gk4KZL3u4zWNQpw31ofz5SO8y+A5yj/V4HHrvUx9X2swAnA3cDXh9sq8G/AEWt9TNP4XYfpfhn4e2DrWh/LtLbapR3XzwJfMcbcYQOGs1V/BvymMeZvI3n/HXjW0P1cwNelmBd1Oc4/pZm4QESeAGxivt/E0epYjTFfMsYcbow52hhzNM1bvZ9ijPl/M2hzW7X+XUXk52nGKk8xxjw47YaulSrwxvVyJrsDvw78CHC+mvY/HEBEPiAi9i0UrwLeLSI3A/+d4Svr51RdjvOPgceLyJcZvgvRDM2DOVWXY11v6nKsf0TzCYdPDtNcOrNWz1D10bKqqqqFUbXwqqqqFkYVeFVVVQujCryqqqqFUQVeVVXVwqgCr6qqamFUgVdVVbUwqsCrqqpaGFXgVVVVLYwq8KqqqhZGFXhVVVULowq8qqqqhVEFXlVV1cKoAq+qqmphVIFXVVW1MKrAq6qqWhhV4FVVVS2MKvCqqqoWRhV4VVVVC6MKvKqqqoXR3H2X9udFzIFPYImM76cZ1ib/LNqVCitt61q3vSQPYD+5oj+94obF4voKy23DLNuUCstNOw9tDbk5/naeuvUo9u3bd70x5ufpqLkD3j3A3qUlmPUGs69z1m1eXl43bTYIgwFzsUE8fnV17dtY2uZ53Hxtlg+9mr1PfR8iclgffKld2qqqqoVRBV5VVdXCaO6+Szv8wPNDa92OqqqqudIWY8yTuhYyd2N4wEPGmPX65feqqqopSET29lFO7dJWVVUtjCrwqqqqFkbzCLzL1roBVVVVc6deuDB3kxZVVVVV09I8WnhVVVVVU1EFXlVV1cJo6sATkWeLyHdF5Kbhdv4w/DgVdpOI3Cciv5Gbfxi3U0Q+LCJfEZHbROSnpn08VVVV/UlEHicinxaRW0XkFhE5x5PmRSLyxeH9v1dE/rMTv0NE7hCRP0rVN6t1eJ81xpysA4wx/wj8BICIHATcCXw0N/9QFwN/YYw5VUQ2AVv7a3JVVdUMtAq8wRjzBRE5BNgnIp80xtyq0nwK+JgxxojIjwN/AvyYiv8d4G9yKpuXLu3PAF81xtyem0FEDgV+GrgcwBiz3xhz73SaV1VVNQ0ZY75pjPnC0P094DbgSCfN/WY0u7oNODDTKiJPBX4I+Muc+mYFvJ8SkZtF5M9F5Ime+JcDVxXmPwb4FvBBEblRRD4gItv6bnhVVdVsJCJHA08GPueJe4mIfAX4M+BXh2FLwLuBN+bWMQvgfQE4yhhzIvCHwJ/qyGFX9BTg2sL8y8BTgEuMMU8GHgB+s+/GV1VVTV8ish34CPAbxpj73HhjzEeNMT8GvJimCwvwX4FPGGPuyK1nKsATkdfYSQZguzHmfgBjzCeAg513Wz0f+IIx5i5fWcaY+wL57wDuMMbYf4MP0wCwqqpqHUlEDqaB3ZXGmP8dS2uM+Rvg8UMG/BTw6yLydeD3gf8iIr8byz+VSQtjzHuB9wKIyBEiIsMBx6fTQPbbKvkriHRnReQI4C43/9D/DRE5bjgB8jPAraFyqqqq5k8iIjTj8LcZY/4gkOZHaMb4jYg8BdhMw4DTVJpfBlaMMdFe3ixmaU8FzhaRVeD7wMvtAORwzO15wKt1BhH5NQBjzKWx/MBrgSuH3eKvAb8yg+OpqqrqTycBpwNfGvYIAX4L2AMHGPBSGuvtYRoGvEwxoEj10bKqqqqF0bwsS6mqqqqauirwqqqqFkYVeFVVVQujCryqqqqFUQVeVVXVwqgCr6qqamFUgVdVVbUw+v+ztHJOm4Os+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hgrid.make_plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "demonstrated-walker",
   "metadata": {},
   "source": [
    "Use the `linear_with_depth` constructor of the `ManningsN` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "opposed-horizon",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyschism.mesh.fgrid import ManningsN\n",
    "mannings = ManningsN.linear_with_depth(hgrid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "amino-communications",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fca1b61ee80>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "ax = plt.tricontourf(mannings.triangulation, mannings.values)\n",
    "plt.gca().axis('scaled')\n",
    "plt.colorbar()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "infrared-inventory",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shinacock Inlet Coarse Grid\n",
      "5780 3070\n",
      "1 -7.2057678270899999E+01 4.0990231694899997E+01 4.6679590072010571E-02\n",
      "2 -7.2052193736299998E+01 4.0971342680500001E+01 4.1903190640077234E-02\n",
      "3 -7.2046968722700001E+01 4.0952380732300000E+01 3.8354036939273570E-02\n",
      "4 -7.2041921976099999E+01 4.0933371100199999E+01 3.5439252416806691E-02\n"
     ]
    }
   ],
   "source": [
    "with open('manning.gr3') as f:\n",
    "    for i in range(6):\n",
    "        print(f.readline(), end='')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
